AIMC Topic: Machine Learning

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Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture.

Neural networks : the official journal of the International Neural Network Society
A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representat...

Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making.

Preventive veterinary medicine
Identifying and understanding the risk factors for endemic bovine tuberculosis (TB) in cattle herds is critical for the control of this disease. Exploratory machine learning techniques can uncover complex non-linear relationships and interactions wit...

Decision tree algorithm in locally advanced rectal cancer: an example of over-interpretation and misuse of a machine learning approach.

Journal of cancer research and clinical oncology
PURPOSE: To analyse the classification performances of a decision tree method applied to predictor variables in survival outcome in patients with locally advanced rectal cancer (LARC). The aim was to offer a critical analysis to better apply tree-bas...

Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.

Epilepsia
OBJECTIVE: Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epileps...

Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics.

IEEE journal of biomedical and health informatics
OBJECTIVE: Histologic grade and Ki-67 proliferation status are important clinical indictors for breast cancer prognosis and treatment. The purpose of this study is to improve prediction accuracy of these clinical indicators based on tumor radiomic an...

Medical knowledge embedding based on recursive neural network for multi-disease diagnosis.

Artificial intelligence in medicine
The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, i...

SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Question answering (QA), the identification of short accurate answers to users questions written in natural language expressions, is a longstanding issue widely studied over the last decades in the open-domain. However, it s...

A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices.

Sensors (Basel, Switzerland)
Teleception is defined as sensing that occurs remotely, with no physical contact with the object being sensed. To emulate innate control systems of the human body, a control system for a semi- or fully autonomous assistive device not only requires fe...

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.

Sensors (Basel, Switzerland)
Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, a...